namespace mlir::quantfork {

static bool getDefaultStorageParams(unsigned numBits, bool narrowRange,
                                    bool isSigned, MLIRContext *ctx,
                                    Type &storageType, int64_t &qmin,
                                    int64_t &qmax) {
  // Hard-coded type mapping from TFLite.
  if (numBits <= 4) {
    storageType = IntegerType::get(ctx, 4);
    if (isSigned) {
      qmin = -8;

//
// Specifically, it combines the following concerns, each of which would be
// independent variables in a more generic setup:
//   - numBits and isSigned imply storage data type (uint8, int8, int16)
//   - numBits < 8 is promoted to uint8 or int8
//   - "narrow_range" narrows the lower bound of the storage type's range by
//     1

      const int64_t int_value = ap_int_value.getSExtValue();

      const float real = (int_value - zp) * scale;

      auto real_int = absl::bit_cast<int32_t>(real);
      return APInt(/*numBits=*/32, real_int);
    };

    auto dequant_values =
        mlir::cast<DenseIntOrFPElementsAttr>(input_values)
            .mapValues(FloatType::getF32(rewriter.getContext()),

quant::QuantParams DefaultQuantParamsPass::GetDefaultQuantParams(
    Builder builder) {
  if (!default_quant_params_) {
    default_quant_params_ = quantfork::fakeQuantAttrsToType(
        builder.getUnknownLoc(),
        /*numBits=*/8, default_min_, default_max_, /*narrowRange=*/false,
        builder.getF32Type(), is_signed_);
  }
  return default_quant_params_;
}

// Creates an instance of the default quant parameters pass.

// CHECK-NEXT:     } ]
// CHECK-NEXT:     signature_defs: [ ]
// CHECK-NEXT:   }

// IMPORT: "tfl.fake_quant"(%arg0) <{max = 1.400000e+00 : f32, min = 3.000000e-01 : f32, narrow_range = false, num_bits = 6 : i32}>

  %0 = "tfl.fake_quant"(%arg0) {num_bits = 6 : i32, narrow_range = false, min = 0.3:f32, max = 1.4:f32} : (tensor<4 x f32>) -> tensor<4 x f32>
  func.return %0 : tensor<4xf32>

      quant_dim = mlir::cast<ShapedType>(res.getType()).getRank() - 1;
    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());

      quant_dim = mlir::cast<ShapedType>(res.getType()).getRank() - 1;
    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());

      quant_dim = input_type.getRank() - 1;
    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());